Data for the comparison

This experiment aims to compare the performance of the AmazonForecast automated solutions against classical statistical models using StatsForecast using the M5 and M4 datasets.

In this notebook we will explain the data used in the experiment.

import pandas as pd

from statsforecast import StatsForecast

/home/ubuntu/fede/statsforecast/statsforecast/core.py:21: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
  from tqdm.autonotebook import tqdm

M5 dataset

Target data

train_df =  pd.read_parquet('s3://m5-benchmarks/data/train/target.parquet')

train_df.head()

	item_id	timestamp	demand
0	FOODS_1_001_CA_1	2011-01-29	3.0
1	FOODS_1_001_CA_1	2011-01-30	0.0
2	FOODS_1_001_CA_1	2011-01-31	0.0
3	FOODS_1_001_CA_1	2011-02-01	1.0
4	FOODS_1_001_CA_1	2011-02-02	4.0

train_df = train_df.rename(columns={'item_id': 'unique_id', 
                                    'timestamp': 'ds',
                                    'demand': 'y'})

StatsForecast.plot(train_df)

Static variables

static_df = pd.read_parquet('s3://m5-benchmarks/data/train/static.parquet')

static_df.head()

	item_id	sku_id	dept_id	cat_id	store_id	state_id
0	FOODS_1_001_CA_1	FOODS_1_001	FOODS_1	FOODS	CA_1	CA
1	FOODS_1_001_CA_2	FOODS_1_001	FOODS_1	FOODS	CA_2	CA
2	FOODS_1_001_CA_3	FOODS_1_001	FOODS_1	FOODS	CA_3	CA
3	FOODS_1_001_CA_4	FOODS_1_001	FOODS_1	FOODS	CA_4	CA
4	FOODS_1_001_TX_1	FOODS_1_001	FOODS_1	FOODS	TX_1	TX

Temporal variables

temporal_df = pd.read_parquet('s3://m5-benchmarks/data/train/temporal.parquet')

temporal_df.head()

	item_id	timestamp	snap_CA	snap_TX	snap_WI	sell_price
0	FOODS_1_001_CA_1	2011-01-29	0.0	0.0	0.0	2.0
1	FOODS_1_001_CA_1	2011-01-30	0.0	0.0	0.0	2.0
2	FOODS_1_001_CA_1	2011-01-31	0.0	0.0	0.0	2.0
3	FOODS_1_001_CA_1	2011-02-01	1.0	1.0	0.0	2.0
4	FOODS_1_001_CA_1	2011-02-02	1.0	0.0	1.0	2.0

temporal_df = temporal_df.rename(columns={'item_id': 'unique_id', 
                                          'timestamp': 'ds'})

StatsForecast.plot(train_df, temporal_df)

M4 Daily dataset

train_df = pd.read_parquet('s3://m4-benchmarks/data/train/target.parquet')

train_df.head()

	item_id	timestamp	target_value
0	D1	2019-03-18	1017.1
1	D1	2019-03-19	1019.3
2	D1	2019-03-20	1017.0
3	D1	2019-03-21	1019.2
4	D1	2019-03-22	1018.7

train_df = train_df.rename(columns={'item_id': 'unique_id', 
                                    'timestamp': 'ds',
                                    'target_value': 'y'})

StatsForecast.plot(train_df)

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